PaddleOCR/configs/det/det_r18_vd_ct.yml

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YAML

Global:
use_gpu: true
epoch_num: 600
log_smooth_window: 20
print_batch_step: 10
save_model_dir: ./output/det_ct/
save_epoch_step: 10
# evaluation is run every 2000 iterations
eval_batch_step: [0,1000]
cal_metric_during_train: False
pretrained_model: ./pretrain_models/ResNet18_vd_pretrained.pdparams
checkpoints:
save_inference_dir:
use_visualdl: False
infer_img: doc/imgs_en/img623.jpg
save_res_path: ./output/det_ct/predicts_ct.txt
Architecture:
model_type: det
algorithm: CT
Transform:
Backbone:
name: ResNet_vd
layers: 18
Neck:
name: CTFPN
Head:
name: CT_Head
in_channels: 512
hidden_dim: 128
num_classes: 3
Loss:
name: CTLoss
Optimizer:
name: Adam
lr: #PolynomialDecay
name: Linear
learning_rate: 0.001
end_lr: 0.
epochs: 600
step_each_epoch: 1254
power: 0.9
PostProcess:
name: CTPostProcess
box_type: poly
Metric:
name: CTMetric
main_indicator: f_score
Train:
dataset:
name: SimpleDataSet
data_dir: ./train_data/total_text/train
label_file_list:
- ./train_data/total_text/train/train.txt
ratio_list: [1.0]
transforms:
- DecodeImage:
img_mode: RGB
channel_first: False
- CTLabelEncode: # Class handling label
- RandomScale:
- MakeShrink:
- GroupRandomHorizontalFlip:
- GroupRandomRotate:
- GroupRandomCropPadding:
- MakeCentripetalShift:
- ColorJitter:
brightness: 0.125
saturation: 0.5
- ToCHWImage:
- NormalizeImage:
- KeepKeys:
keep_keys: ['image', 'gt_kernel', 'training_mask', 'gt_instance', 'gt_kernel_instance', 'training_mask_distance', 'gt_distance'] # the order of the dataloader list
loader:
shuffle: True
drop_last: True
batch_size_per_card: 4
num_workers: 8
Eval:
dataset:
name: SimpleDataSet
data_dir: ./train_data/total_text/test
label_file_list:
- ./train_data/total_text/test/test.txt
ratio_list: [1.0]
transforms:
- DecodeImage:
img_mode: RGB
channel_first: False
- CTLabelEncode: # Class handling label
- ScaleAlignedShort:
- NormalizeImage:
order: 'hwc'
- ToCHWImage:
- KeepKeys:
keep_keys: ['image', 'shape', 'polys', 'texts'] # the order of the dataloader list
loader:
shuffle: False
drop_last: False
batch_size_per_card: 1
num_workers: 2